Graduate Thesis Or Dissertation
 

Iterative reconstruction methods of CT images using a statistical framework

Public Deposited

Downloadable Content

Download PDF
https://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/m613n2759

Descriptions

Attribute NameValues
Creator
Abstract
  • Medical imaging technologies play a vital role in early diagnosis of disease by providing internal images of the human body to medical professionals. Computed Tomography (CT) is currently the most commonly used medical imaging technology because it is easy to use, detectors and scanners are constantly improving, and more importantly, patients receive less radiation compared to other imaging technologies. This thesis focuses on improving CT reconstruction algorithms by incorporating prior knowledge of the tissues being scanned. A Gaussian Mixture Prior, and Gibbs sampling is introduced into the reconstruction framework and solved using Maximum-a-posterior (MAP). As a comparison, the images were also reconstructed using unregularized and regularized Maximum Likelihood (ML).
License
Resource Type
Date Available
Date Issued
Degree Level
Degree Name
Degree Field
Degree Grantor
Commencement Year
Advisor
Committee Member
Academic Affiliation
Non-Academic Affiliation
Subject
Rights Statement
Publisher
Peer Reviewed
Language
Replaces

Relationships

Parents:

This work has no parents.

In Collection:

Items